More and more organizations are interested in management and infrastructure services, such as automated control systems and analysis and health-monitoring tools, for accelerated or automated decision making. These services require monitored data (or collected data) about the utilization and behavior of the computing environments, that is, monitored data about the implemented information technology (IT) infrastructures. As referred herein, and as understood in the art, information technology, or IT, encompasses all forms of technology, including but not limited to the design, development, installation, and implementation of hardware and software information systems and software applications, used to create, store, exchange and utilize information in its various forms including but not limited to business data, conversations, still images, motion pictures and multimedia presentations technology and with the design, development, installation, and implementation of information systems and applications.
IT monitored data (hereinafter, “monitored data”), includes traditional system measurements or metrics (e.g., CPU utilization, memory utilization), application information (e.g., Web server logs, transactions per second), and environmental information (e.g., power utilization, data center temperature, data center humidity). As referred herein, a metric is any quantity that can be measured about a particular environment, such as an IT infrastructure. Numerous different types of metrics exist. Some metrics are specific to individual system components, such as the utilization of each individual CPU, or of the bandwidth used on a particular network interface. Thus, as also referred herein, monitored data includes measured values about one or more metrics, such as IT metrics. For example, monitored data may include metrics that reflect how specific processes or applications use these components. Other metrics summarize the behavior of an entire category of components, such as the average utilization of all CPUs in the system, or the aggregate input/output (I/O) rate across all disks.
Accordingly, monitored data may be used in automation tools for resource allocation, server consolidation, capacity planning, event correlation analysis, and closed-loop control to improve the accuracy of automated decision making processes employed in such tools. For example, to automatically and dynamically reallocate IT resources in an IT infrastructure to applications to meet business objectives, a resource allocation and capacity planning tool must base its automated resource allocation decisions (such as automatically providing additional resources to satisfy increased demands) on monitored data that reflects the use and behavior of the IT infrastructure. This is because capacity planners seek to ensure that the additional resources are available “just in time.” If the resources are purchased too soon, costs are incurred that could have been delayed. On the other hand, if the resources are not acquired in time, revenues may be lost due to the impact on business processes. In another example, to reduce management and license costs by merging workloads from underutilized systems onto a smaller number of systems, a server consolidation must also base its automated server consolidation decisions on IT data collected from existing systems. The monitored data allows the server consolidation tool to automatically perform an analysis of these systems and make decisions on the consolidation of the workloads onto a smaller set of servers. Basing the automated server consolidation decisions on an analysis of empirical data not only enables better and more accurate decisions, but also allows for customization of such decisions for each particular IT infrastructure or environment. It should be understood that discussions of IT data herein are also applicable to other types of data.